Optimal production planning and scheduling in breweries

This work considers the optimal production planning and scheduling problem in beer production facilities. The underlying optimization problem is characterized by significant complexity, including multiple production stages, several processing units, shared resources, tight design and operating constraints and intermediate and final products. Breweries are mainly differentiated to the rest of the beverage industries in terms of long lead times required for the fermentation/maturation process of beer. Therefore, synchronizing the production stages is an extremely challenging task, while the long time horizon leads to larger and more difficult optimization problems. In this work we present a new MILP model, using a mixed discrete-continuous time representation and the immediate precedence framework in order to minimize total production costs. A number of test cases are used to illustrate the superiority of the proposed model in terms of computational efficiency and solution quality compared with approaches developed in other research contributions. The proposed model provides consistently better solutions and improvements of up to SO% are reported. In order to address large-scale problem instances and satisfy the computation limitations imposed by the industry, a novel MILP-based solution strategy is developed, that consists of a constructive and an improvement step. As a result, near-optimal solutions for extremely large cases consisting of up to 30 fermentation tanks, 5 filling lines and 40 products are generated in less than two hours. Finally, the proposed method is successfully applied to a real-life case study provided by a Greek brewery and near-optimal schedules are generated in relatively short CPU times. (C) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

Automated summary

This study focuses on the optimal production planning and scheduling problem in beer production facilities. By proposing a novel MILP model, the total production costs are minimized efficiently, leading to improvements in both computational efficiency and solution quality.